Dataset distillation has emerged as a strategy to overcome the hurdles associated with large datasets by learning a compact set of synthetic data that retains essential information from the original dataset.
Jun 6, 2024
May 24, 2023 · Dataset distillation is a technique in machine learning that concerns selecting a representative subset of data samples from a larger dataset.
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Oct 7, 2023 · Dataset distillation (DD) aims distilling knowledge of the dataset into some synthetic data while preserving the per- formance of the models ...
Dec 15, 2021 · Dataset distillation can be formulated as a two-stage optimization process: an “inner loop” that trains a model on learned data, and an “outer ...
Dataset distillation is the task of synthesizing a small dataset such that models trained on it achieve high performance on the original large dataset.
Dataset distillation (DD) aims distilling knowledge of the dataset into some synthetic data while preserving the performance of the models trained on it. Its ...
Dataset Distillation refers to the curation of a small, syn- thetic training set Dsyn such that a model trained on this synthetic data will have similar ...
Dataset distillation is the task of synthesizing a small dataset such that models trained on it achieve high performance on the original large dataset.
Dataset Distillation is a newly emerging area that aims to distill large datasets into much smaller and highly informative synthetic ones to accelerate ...
Aug 7, 2022 · Dataset distillation is the task of synthesizing a small dataset such that models trained on it achieve high performance on the original large dataset.
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